AI agents, automation & chatbots
Build AI agents that do real work — with named human control where it matters.
We design and implement AI agents, chatbots and automation workflows inside your existing systems, then add named human review, escalation and specialist ownership wherever the process needs judgement.
Use your approved tools · keep named human review where it matters · document the handoff
AI agents & automation
Live workflow
ExecutionAI + automation
ControlHuman-owned
IntelligenceMeasurable
VA20Four7 ownsRepeatable AI-assisted execution
Your team keepsPolicy and risk decisions
One operating model
Execution. Control. Intelligence.
Three connected layers keep the work moving without turning automation into another black box.
ExecutionAI agents + automation
Handle the stable, repeatable steps.
ControlHuman owners
Approve, resolve exceptions and own sensitive judgement.
IntelligenceQA + customer signals
Measure quality, workflow health and what users are telling you.
What the workflow can own
Turn AI from isolated tools into an operating layer the business can rely on.
We start with the workflow, automate the repeatable steps, design the agent behaviour and integrations, then define exactly where humans review, approve, escalate or take over.
Workflow mapping and AI task design
Break recurring work into steps and identify where AI can assist without removing the judgement, approvals or accountability the process needs.
AI-assisted research and drafting
Use approved AI tools for first-pass research, summaries, drafts and repeatable preparation while a specialist verifies the result before it is used.
Document and data processing
Support classification, extraction, summaries, cleanup and routing for recurring document-heavy work, with checks against the source where accuracy matters.
Knowledge and support operations
Maintain knowledge, prepare suggested replies, summarize conversations and improve handoffs between self-service, AI and human teams.
Lightweight automation and routing
Reduce repetitive copy-paste and manual handoffs using the automation capabilities already available in your stack where the workflow is stable enough to support it.
Human QA and exception handling
Define what gets checked, what gets escalated and what always stays human-owned so faster execution does not create a new quality problem.
Execution + control
Automate what is stable. Keep human ownership where judgement begins.
The workflow separates repeatable execution from the moments that require a person. AI agents and automation handle stable steps; a named human owner handles judgement, approvals, exceptions and final accountability.
AI agents & automationWorkflow view
01Trigger→02Agent executes→03Human control→04Measured outcome
ControlExceptions stay human-ownedOutcomeVisible and measurable
Intelligence layer
Do not just run the workflow. See how it performs.
The platform brings AI-agent and human-owner performance, Auto QA, scorecards and operational signals into one view. For customer-facing workflows, it also surfaces sentiment, pain points, feature requests and recurring conversation themes.
Explore the platform →Performance intelligenceLive platform
Create a repeatable quality layer around AI and human execution.
Measure workflow quality, completion and review performance.
See where automation needs human intervention most often.
Track completion, bottlenecks and recurring friction.
What changes
Less manual coordination. More visible execution.
Capacity matters, but the bigger gain is a workflow with clearer ownership, better handoffs and a measurable path from request to outcome.
Work you can actually see
A record of what ran automatically, what a person checked and where the workflow needed a human decision.
Measurement built in
Because we also build the measurement side, you get a clear record of what was done, what was automated and what a person checked — rather than a monthly summary written by the person being reviewed.
Less repetitive manual work
AI assists with high-volume preparation, summaries and routine processing so specialists can spend more time on judgement, follow-through and exceptions.
AI use becomes repeatable
Prompts, review steps, handoffs and escalation rules become part of the documented workflow instead of living in one person’s browser history.
Quality stays owned
A named person remains responsible for checking context, handling edge cases and making sure the final output is suitable for the business.
Fits your stackWorks around the systems you already use.
Your approved AI toolsGoogle Workspace / Microsoft 365CRM & helpdesk systemsKnowledge basesAutomation tools already in your stack
How we start
One workflow first. Prove the model, then expand.
We map the work, define what AI can execute safely, place the named human control points and launch with a contained scope before expanding.
Map
Understand the workload and current handoffs.
Design
Define AI execution, named human review and escalation.
Launch
Run the workflow inside your existing systems.
Improve
Use QA and performance signals to refine it.
Questions
Questions before you start
Clear answers before you decide whether the model fits.
Do we need people at all if the workflow is automated?+
Usually yes, and fewer of them. Automation handles the volume; someone still owns the exceptions, the quality and the answer when a customer asks why something went wrong. Engagements that skip that part are the ones that quietly fail.
What happens when the automation breaks?+
It will, eventually — an API changes, a format shifts, an edge case arrives. The workflow is built so failures surface to a named person rather than failing silently, which is the difference between an automation you can trust and one you find out about in a customer complaint.
Do you have a platform for measuring the agents too?+
Yes. VA20Four7 Intelligence brings AI and human agent performance, Auto QA, scorecards, sentiment, pain points and feature requests into one operating view.
Does AI make decisions without review?+
Not by default. We define the level of human review around the risk and context of the task. Sensitive, high-impact or exception-driven decisions stay with the appropriate person.
Can you work with the AI tools we already use?+
Yes, where your access, security and internal policies allow it. The goal is to fit into your operating environment rather than force a separate AI stack.
Can we start with one workflow?+
Yes. A single repetitive workflow is often the best place to start because it is easier to define the current process, measure whether the change helps and refine the human-AI handoff.
What data should be used with AI?+
Only data your organization has approved for the relevant tool and workflow. Access, permissions and sensitive-data boundaries should be defined before the process goes live.
Start with the workload
Ready to build a workflow that holds up?
Tell us which process is costing the most time. We will map it, tell you what should be automated, and own what comes out the other end.